Scott discusses three recent papers showing surprising patterns in how AI misbehavior does and doesn't generalize across different contexts, from emergent misalignment spreading across domains to reward-hacking staying confined to graded tasks.

Scott argues that AI 'hallucinations' should be called 'shameless guesses' because they work the same way as students guessing on tests - making their best attempt when uncertain rather than admitting ignorance, revealing an alignment problem.

Scott explores three approaches to 'writing for AI' - teaching knowledge, influencing beliefs, and enabling simulation - finding the first limited, the second theoretically confused, and the third creepy and ethically troubling.

Scott Alexander discusses Janus' experiments with GPT-3, exploring its capabilities, quirks, and potential implications.